Reconstruction Technique

Reconstruction techniques aim to recover an original signal or image from incomplete or noisy measurements, a crucial task across diverse scientific fields. Current research emphasizes developing efficient and accurate algorithms, particularly leveraging deep learning architectures like U-shaped convolutional networks and implicit neural representations (INRs), to address challenges such as real-time processing on mobile devices and handling complex phenomena like metal artifacts in CT scans or refraction in transparent object imaging. These advancements improve the quality and speed of reconstructions in applications ranging from medical imaging to computer vision, impacting both scientific understanding and practical applications.